npj Computational Materials最新文献_第4页

Learning to predict rare events: the case of abnormal grain growth 学习预测罕见事件：异常晶粒生长的案例

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-27 DOI: 10.1038/s41524-025-01530-8

Houliang Zhou, Benjamin Zalatan, Joan Stanescu, Martin P. Harmer, Jeffrey M. Rickman, Lifang He, Christopher J. Marvel, Brian Y. Chen

{"title":"Learning to predict rare events: the case of abnormal grain growth","authors":"Houliang Zhou, Benjamin Zalatan, Joan Stanescu, Martin P. Harmer, Jeffrey M. Rickman, Lifang He, Christopher J. Marvel, Brian Y. Chen","doi":"10.1038/s41524-025-01530-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01530-8","url":null,"abstract":"Abnormal grain growth (AGG) in polycrystalline microstructures, characterized by the rapid and disproportionate enlargement of a few “abnormal” grains relative to their surroundings, can lead to dramatic, often deleterious changes in the mechanical properties of materials, such as strength and toughness. Thus, the prediction and control of AGG is key to realizing robust mesoscale materials design. Unfortunately, it is challenging to predict these rare events far in advance of their onset because, at early stages, there is little to distinguish incipient abnormal grains from “normal” grains. To overcome this difficulty, we propose two machine learning approaches for predicting whether a grain will become abnormal in the future. These methods analyze grain properties derived from the spatio-temporal evolution of grain characteristics, grain-grain interactions, and a network-based analysis of these relationships. The first, PAL (Predicting Abnormality with LSTM), analyzes grain features using a long short-term memory (LSTM) network, and the second, PAGL (Predicting Abnormality with GCRN and LSTM), supplements the LSTM with a graph-based convolutional recurrent network (GCRN). We validated these methods on three distinct material scenarios with differing grain properties, observing that PAL and PAGL achieve high sensitivity and precision and, critically, that they are able to predict future abnormality long before it occurs. Finally, we consider the application of the deep learning models developed here to the prediction of rare events in different contexts.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unintuitive alloy strengthening by addition of weaker elements 通过添加弱元素来增强合金的不直观性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-27 DOI: 10.1038/s41524-025-01576-8

Dharmendra Pant, Dilpuneet S. Aidhy

引用次数: 0

Latent Ewald summation for machine learning of long-range interactions 远程相互作用机器学习的潜埃瓦尔德求和

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-26 DOI: 10.1038/s41524-025-01577-7

Bingqing Cheng

引用次数: 0

Development of an atomic cluster expansion potential for iron and its oxides 铁及其氧化物原子团簇膨胀势的发展

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-26 DOI: 10.1038/s41524-025-01574-w

Baptiste Bienvenu, Mira Todorova, Jörg Neugebauer, Dierk Raabe, Matous Mrovec, Yury Lysogorskiy, Ralf Drautz

引用次数: 0

Applications of natural language processing and large language models in materials discovery 自然语言处理和大语言模型在材料发现中的应用

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-24 DOI: 10.1038/s41524-025-01554-0

Xue Jiang, Weiren Wang, Shaohan Tian, Hao Wang, Turab Lookman, Yanjing Su

引用次数: 0

Phase-field modeling of coupled bulk photovoltaic effect and ferroelectric domain manipulation at ultrafast timescales 超快时间尺度下耦合体光伏效应和铁电畴操纵的相场建模

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-20 DOI: 10.1038/s41524-025-01556-y

Yi-De Liou, Kena Zhang, Ye Cao

{"title":"Phase-field modeling of coupled bulk photovoltaic effect and ferroelectric domain manipulation at ultrafast timescales","authors":"Yi-De Liou, Kena Zhang, Ye Cao","doi":"10.1038/s41524-025-01556-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01556-y","url":null,"abstract":"The bulk photovoltaic (BPV) effect, which generates steady photocurrents and above-bandgap photovoltages in non-centrosymmetric materials when exposed to light, holds great potential for advancing optoelectronic and photovoltaic technologies. However, its influence on the reconfiguration of ferroelectric domain structure remains underexplored. In this study, we developed a phase-field model to understand the BPV effect in ferroelectric oxides. Our model reveals that variations in BPV currents across domains create opposing charges at domain walls, enhancing the electric field within domains to ~1000 kV/cm. The strong electric fields can reorient the ferroelectric polarization and enable ultrafast domain wall movements and nonvolatile domain switching on the picosecond scale. Applying anisotropic strain can further strengthen this effect, enabling more precise control of domain switching. Our findings advance the fundamental understanding of BPV effect in ferroelectrics, paving the ways for developing opto-ferroelectric memory technologies and high-efficiency photovoltaic applications via precise domain engineering.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comment on “Machine learning enhanced analysis of EBSD data for texture representation” 对“机器学习增强的纹理表示EBSD数据分析”的评论

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-19 DOI: 10.1038/s41524-025-01557-x

Helmut Schaeben, K. Gerald van den Boogaart

引用次数: 0

Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys 提高高熵合金分类和回归模型性能的元素数值描述

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-18 DOI: 10.1038/s41524-025-01560-2

Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

{"title":"Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys","authors":"Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su","doi":"10.1038/s41524-025-01560-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01560-2","url":null,"abstract":"The machine learning-assisted design of new alloy compositions often relies on the physical and chemical properties of elements to describe the materials. In the present study, we propose a strategy based on an evolutionary algorithm to generate new elemental numerical descriptions for high-entropy alloys (HEAs). These newly defined descriptions significantly enhance classification accuracy, increasing it from 77% to ~97% for recognizing FCC, BCC, and dual phases, compared to traditional empirical features. Our experimental validation demonstrates that our classification model, utilizing these new elemental numerical descriptions, successfully predicted the phases of 8 out of 9 randomly selected alloys, outperforming the same model based on traditional empirical features, which correctly predicted 4 out of 9. By incorporating these descriptions derived from a simple logistic regression model, the performance of various classifiers improved by at least 15%. Moreover, these new numerical descriptions for phase classification can be directly applied to regression model predictions of HEAs, reducing the error by 22% and improving the R2 value from 0.79 to 0.88 in hardness prediction. Testing on six different materials datasets, including ceramics and functional alloys, demonstrated that the obtained numerical descriptions achieved higher prediction precision across various properties, indicating the broad applicability of our strategy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"55 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures 利用高通量分子模拟和机器学习来设计化学混合物

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-17 DOI: 10.1038/s41524-025-01552-2

Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M. Collins, Suraj Gattani, Mayank Misra, Anand Chandrasekaran, Karl Leswing, Mathew D. Halls

{"title":"Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures","authors":"Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M. Collins, Suraj Gattani, Mayank Misra, Anand Chandrasekaran, Karl Leswing, Mathew D. Halls","doi":"10.1038/s41524-025-01552-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01552-2","url":null,"abstract":"Mixtures of chemical ingredients, such as formulations, are ubiquitous in materials science, but optimizing their properties remains challenging due to the vast design space. Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation. Using high-throughput classical molecular dynamics simulations, we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property: formulation descriptor aggregation (FDA), formulation graph (FG), and Set2Set-based method (FDS2S). Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties. Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing. The models show robust transferability to experimental datasets, accurately predicting properties across energy, pharmaceutical, and petroleum applications. Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"89 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spin-splitting above room-temperature in Janus Mn2ClSeH antiferromagnetic semiconductor with a large out-of-plane piezoelectricity 具有大面外压电的Janus Mn2ClSeH反铁磁半导体室温以上的自旋分裂

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-17 DOI: 10.1038/s41524-025-01566-w

Haiming Lu, Sitong Bao, Bocheng Lei, Sutao Sun, Linglu Wu, Jian Zhou, Lili Zhang

{"title":"Spin-splitting above room-temperature in Janus Mn2ClSeH antiferromagnetic semiconductor with a large out-of-plane piezoelectricity","authors":"Haiming Lu, Sitong Bao, Bocheng Lei, Sutao Sun, Linglu Wu, Jian Zhou, Lili Zhang","doi":"10.1038/s41524-025-01566-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01566-w","url":null,"abstract":"Two-dimensional (2D) antiferromagnets have garnered considerable research interest due to their robustness against external magnetic perturbation, ultrafast dynamics, and magneto-transport effects. However, the lack of spin-splitting in antiferromagnetic (AFM) materials severely limits their potential in spintronics applications. Inspired by inherent out-of-plane potential gradient of Janus structure, we predict three stable AFM Janus Mn2ClXH (X = O, S, and Se) monolayers with spontaneous spin-splitting based on first-principles calculations. Notably, Janus Mn2ClSeH exhibits a high Néel temperature of up to 510 K, robust perpendicular magnetocrystalline anisotropy, outstanding out-of-plane piezoelectricity of 0.454 × 10−10 C/m, and sizeable spontaneous valley polarization of 17.2 meV. Moreover, the spin-splitting can be significantly enhanced through appropriate synergistic regulation of biaxial strain and external electric field. These results demonstrate that the Janus Mn2ClSeH monolayer is a very potential candidate for designing intriguing antiferromagnet-based devices with fantastic piezoelectric and valleytronic characteristics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"39 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0